Physicochemical characterization of water quality in the Perlamayo and Tacamache rivers, using benthic macroinvertebrates as biological indicators in the district of Chugur- Cajamarca
Bibliographic record
Abstract
The general objective of this research was to determine water quality through physicochemical and sediment analysis.Also, to evaluate the benthic macroinvertebrate community through biotic indices (EPT, BMWP/Bol, BMWP/Col, ABI and CERA) in the Perlamayo and Tacamache rivers.Seven monitoring points were established according to the degree of intervention by anthropogenic activity.During the months of February (rainy season) and July (dry season), the results of the physicochemical variables were compared to Peruvian Environmental Quality Standards.According to Supreme Decree No. 004-20017-MIMAN, and Sediments compared to the Canadian ECA TEL (Threshold Effect Level).To study the relationship between macroinvertebrate communities and environmental variables.An statistical analysis was carried out to determine which pressures most affect the benthic community, using a simple regression model.The results obtained show that the distribution and composition of the benthic community is determined by the physicochemical parameters of temperature, pH, conductivity, dissolved oxygen and the concentrations of heavy metals.Six orders and 16 families of benthic macroinvertebrates were identified.Including, Leptophlebiidae, Gripopterygidae, Leptoceridae, Hydrobiosidae, Hyalellidae and Chironomidae; the ETP index gave a poor water quality; the BMWP/Bol and BMWP/Col indices gave a critical quality; the ABI and CERA indices showed a moderate quality.In conclusion, the levels of alteration evaluated in this research are a useful tool for determining the ecological quality of the Perlamayo and Tacamache river systems.Proving to be an appropriate method for the evaluation and monitoring of other watersheds in the Cajamarca region, Peru.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".